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mitral valve (mv) repair is the preferred treatment for patients with mv insufficiency. The unsolved problem in MV repair surgery is predicting the optimal repair for each patient. This is in part due to lack of physiological imaging modalities to provide this information prior to or at the time of valve repair. Moreover, the majority of cases have complex pathophysiological involvement including annular enlargement, chordal lengthening, chordal rupture, calcification, and ultimately lack of proper leaflet coaptation.

Current clinical 3-dimensional (3D) transesophageal echocardiography (TEE) can demonstrate volumetric morphology of the MV apparatus. However, biomechanical information is not available from 3D echocardiography. If imaging techniques can be combined with appropriate computational MV evaluation methods, then improved diagnosis and therapeutic approaches to MV repair can be developed. In the present study, we describe a novel comprehensive evaluation protocol to improve diagnosis and treatment of MV pathology by combining 3D TEE and computational simulation techniques (Fig. 1). Virtual MV models were created by utilizing 3D TEE data of patients with normal and pathological MVs followed by computational simulations of MV function. Computational simulations clearly demonstrated deformation and stress distribution of the MV structure across the cardiac cycle at a microsecond scale and corresponded well to 3D TEE data (Figs. 2 and 3,Online Videos 1, 2, 3, and 4). Here we present 4 case studies (1 normal and 3 different types of pathological MVs).

Case 3 (Fig. 6): a degenerative MV with large annular dilation demonstrated severe regurgitation by 3D Doppler TEE and the lesion corresponded to regions with no leaflet contact in the computational simulation.

Although MV morphology obtained with 3D TEE image data may demonstrate relatively normal function with no regurgitation, the leaflets may be under extremely high stresses which can result in annular dilation and MV deterioration. Biomechanical information from computational simulation further provides information to help better understand MV pathophysiology. This novel computational strategy has the potential to predict pathophysiological alterations in MV structure, help cardiologists to quantitatively evaluate the extent and severity of MV pathology, and help surgeons to better understand MV dynamics before and following repair to determine more suitable patient-specific repair techniques.

Appendix

For expanded figure captions and supplementary videos and their legends, please see the online version of this article.

Appendix

Footnotes

This work was supported in part by the National Institutes of Health (R01 HL109597 to Dr. Kim). All authors have reported that they have no relationships relevant to the contents of this paper to disclose.